A BAYESIAN-APPROACH TO AUTOCALIBRATION FOR PARAMETRIC ARRAY SIGNAL-PROCESSING

A number of techniques for parametric (high-resolution) array signal p rocessing have been proposed in the last few decades. With few excepti ons, these algorithms require an exact characterization of the array, including knowledge of the sensor positions, sensor gain/phase respons e, mutual coupling, and receiver equipment effects. Unless all sensors are identical, this information must typically be obtained by experim ental measurements (calibration). In practice, of course, all such inf ormation is inevitably subject to errors. Recently, several different methods have been proposed for alleviating the inherent sensitivity of parametric methods to such modeling errors. The technique proposed he rein is related to the class of so-called auto-calibration procedures, but it is assumed that certain prior knowledge of the array response errors is available. This is a reasonable assumption in most applicati ons, and it allows for more general perturbation models than does pure auto-calibration. The optimal maximum a posteriori (MAP) estimator fo r the problem at hand is formulated, and a computationally more attrac tive large-sample approximation is derived. The proposed technique is shown to be statistically efficient, and the achievable performance is illustrated by numerical evaluation and computer simulation.